Incremental quantile tracking of multiple record types
    42.
    发明授权
    Incremental quantile tracking of multiple record types 有权
    多种记录类型的增量分位数跟踪

    公开(公告)号:US08666946B2

    公开(公告)日:2014-03-04

    申请号:US12546344

    申请日:2009-08-24

    CPC classification number: G06F17/18

    Abstract: A method and apparatus are provided for incrementally tracking quantiles in the presence of multiple record types. A method for performing incremental quantile tracking includes receiving a first data record of a first record type and a second data record of a second record type, and updating a quantile probability for a quantile value, based on the first record type of the first data record and the second record type of the second data record, to obtain a new quantile probability for the quantile value.

    Abstract translation: 提供了一种用于在存在多种记录类型的情况下递增跟踪分位数的方法和装置。 一种用于执行增量分位数跟踪的方法包括:接收第一记录类型的第一数据记录和第二记录类型的第二数据记录,并且基于第一数据记录的第一记录类型更新分位数值的分位数概率 和第二数据记录的第二记录类型,以获得分位数值的新的分位数概率。

    Efficient probabilistic counting scheme for stream-expression cardinalities
    43.
    发明授权
    Efficient probabilistic counting scheme for stream-expression cardinalities 有权
    流表示基数的有效概率计数方案

    公开(公告)号:US08400933B2

    公开(公告)日:2013-03-19

    申请号:US12110380

    申请日:2008-04-28

    CPC classification number: H04L41/142 H04L43/026

    Abstract: In one embodiment, a method of monitoring a network. The method includes, at each node of a fixed set, constructing a corresponding vector of M components based on data packets received at the node during a time period, M being an integer greater than 1, the fixed set being formed of some nodes of the network; and, based on the constructed vectors, estimating how many of the received data packets have been received by all of the nodes of the set or estimating how many flows of the received data packets have data packets that have passed through all of the nodes of the set. The constructing includes updating a component of the vector of one of the nodes in response to the one of the nodes receiving a data packet. The updating includes selecting the component for updating by hashing a property of the data packet received by the one of the nodes.

    Abstract translation: 在一个实施例中,一种监视网络的方法。 该方法包括:在固定集合的每个节点处,基于在一段时间内在节点处接收到的数据分组来构造M个分量的相应向量,M是大于1的整数,该固定集合由 网络; 并且基于所构建的向量,估计所集合的所有节点已经接收到多少接收到的数据分组,或者估计接收到的数据分组的多少流具有已经通过所有节点的数据分组 组。 所述构造包括响应于接收到数据分组的所述节点之一更新所述节点之一的向量的分量。 该更新包括通过对由该节点之一接收到的数据分组的属性进行哈希来选择用于更新的分量。

    Estimation method for loss rates in a packetized network
    44.
    发明授权
    Estimation method for loss rates in a packetized network 有权
    分组网络中丢失率的估计方法

    公开(公告)号:US08274902B2

    公开(公告)日:2012-09-25

    申请号:US12462965

    申请日:2009-08-12

    Applicant: Tian Bu Jin Cao

    Inventor: Tian Bu Jin Cao

    CPC classification number: H04W24/08 H04L43/0835 H04W84/042 H04W88/18

    Abstract: A method is provided, according to which data are collected on downstream packet losses at a single point in a network. From from the collected data, packet loss rates are estimated on at least two subnetworks downstream of the collection point. The respective subnetworks may differ by one or more links.

    Abstract translation: 提供了一种方法,根据该方法,在网络中的单个点处收集关于下游分组丢失的数据。 从收集的数据中,在收集点下游的至少两个子网络上估计丢包率。 相应的子网可以由一个或多个链路来区分。

    Estimation method for loss rates in a packetized network
    45.
    发明申请
    Estimation method for loss rates in a packetized network 有权
    分组网络中丢失率的估计方法

    公开(公告)号:US20110038269A1

    公开(公告)日:2011-02-17

    申请号:US12462965

    申请日:2009-08-12

    Applicant: Tian Bu Jin Cao

    Inventor: Tian Bu Jin Cao

    CPC classification number: H04W24/08 H04L43/0835 H04W84/042 H04W88/18

    Abstract: A method is provided, according to which data are collected on downstream packet losses at a single point in a network. From from the collected data, packet loss rates are estimated on at least two subnetworks downstream of the collection point. The respective subnetworks may differ by one or more links.

    Abstract translation: 提供了一种方法,根据该方法,在网络中的单个点处收集关于下游分组丢失的数据。 从收集的数据中,在收集点下游的至少两个子网络上估计丢包率。 相应的子网可以由一个或多个链路来区分。

    METHOD AND APPARATUS FOR INCREMENTAL TRACKING OF MULTIPLE QUANTILES
    46.
    发明申请
    METHOD AND APPARATUS FOR INCREMENTAL TRACKING OF MULTIPLE QUANTILES 有权
    用于增量跟踪多个量子的方法和装置

    公开(公告)号:US20110010327A1

    公开(公告)日:2011-01-13

    申请号:US12546255

    申请日:2009-08-24

    CPC classification number: G06F17/18

    Abstract: A method and apparatus for incremental tracking of multiples quantiles is provided. A method for performing an incremental quantile update using a data value of a received data record includes determining an initial distribution function, updating the initial distribution function to form a new distribution function based on the received data value, generating an approximation of the new distribution function, and determining new quantile estimates from the approximation of the new distribution function. The initial distribution function includes a plurality of initial quantile estimates and a respective plurality of initial probabilities. The initial distribution function is updated to form the new distribution function based on the received data value. The new distribution function includes a plurality of quantile points identifying the respective initial quantile estimates and a respective plurality of new probabilities associated with the respective initial quantile estimates. The approximation of the new distribution function is generated by, for each pair of adjacent quantile points in the new distribution function, connecting the adjacent quantile points using a linear approximation of a region between the adjacent quantile points. The new quantile estimates and the new probabilities associated with the new quantile estimates may then be stored.

    Abstract translation: 提供了一种用于增量跟踪多个分位数的方法和装置。 使用接收到的数据记录的数据值来执行增量分位数更新的方法包括确定初始分布函数,基于所接收的数据值更新初始分布函数以形成新的分布函数,生成新分布函数的近似值 ,并根据新分布函数的近似来确定新的分位数估计。 初始分布函数包括多个初始分位数估计和相应的多个初始概率。 基于收到的数据值更新初始分配函数以形成新的分布函数。 新的分布函数包括多个分位点,其识别相应的初始分位数估计以及与各自的初始分位数估计相关联的相应的多个新概率。 新分布函数的近似由新分布函数中的每对相邻分位数点产生,使用相邻分位点之间的区域的线性近似来连接相邻的分位数点。 然后可以存储新的分位数估计值和与新分位数估计值相关联的新概率。

    Method and apparatus for allocating link bandwidth as function of QOS requirement
    47.
    发明授权
    Method and apparatus for allocating link bandwidth as function of QOS requirement 有权
    分配链路带宽作为QOS要求的函数的方法和装置

    公开(公告)号:US07660706B2

    公开(公告)日:2010-02-09

    申请号:US10783806

    申请日:2004-02-20

    CPC classification number: H04L41/145 H04L41/142 H04L43/50 H04L47/15 H04L47/24

    Abstract: A method is provided for determining a bandwidth allocation needed to provide a specified QOS requirement that takes appropriate account of statistical variations for packet streams in a transmission link. In particular, a statistical model of the packet stream is formed using fractional sum difference statistical models and the model is evaluated in respect to synthetically generated traffic streams. The bandwidth allocation approach is specified in terms of the bandwidth, β, required for a traffic load, τ, subject to the requirements of a maximum queuing delay, δ, and a packet loss limitation parameter, ω. Accordingly, that bandwidth allocation approach is implemented as a statistical model for β as a function of τ, δ and ω.

    Abstract translation: 提供了一种用于确定提供指定的QOS要求所需的带宽分配的方法,该规定对传输链路中的分组流进行统计变化的适当考虑。 特别地,使用小数和差分统计模型形成分组流的统计模型,并且对于综合生成的业务流来评估模型。 按照最大排队延迟,增量和分组丢失限制参数Ω的要求,流量负载τ所需的带宽,beta的带宽分配方法被规定。 因此,该带宽分配方法被实现为beta的统计模型,作为tau,delta和ω的函数。

    PROBABILISTIC AGGREGATION OVER DISTRIBUTED DATA STREAMS
    48.
    发明申请
    PROBABILISTIC AGGREGATION OVER DISTRIBUTED DATA STREAMS 有权
    分布式数据流的概率聚合

    公开(公告)号:US20090271509A1

    公开(公告)日:2009-10-29

    申请号:US12110431

    申请日:2008-04-28

    Applicant: Jin Cao Aiyou Chen

    Inventor: Jin Cao Aiyou Chen

    CPC classification number: H04L41/142

    Abstract: In one embodiment, a method of monitoring a network. The method includes, at each node of a set, constructing a corresponding vector of M components based on a stream of data packets received at the node during a time period, the set including a plurality of nodes of the network, M being greater than 1; and estimating a value of a byte traffic produced by a part of the packets based on the constructed vectors, the part being the packets received by every node of the set. The constructing includes updating a component of the vector corresponding to one of the nodes in response to the one of the nodes receiving a data packet. The updating includes selecting a component of the vector to be updated by hashing a property of the received data packet.

    Abstract translation: 在一个实施例中,一种监视网络的方法。 该方法包括在一组的每个节点处,基于在一段时间段内在该节点处接收的数据分组流来构建M个分量的相应向量,该组包括网络的多个节点,M大于1 ; 以及基于构造的向量来估计由部分分组产生的字节流量的值,所述部分是由所述集合的每个节点接收的分组。 所述构造包括响应于接收到数据分组的所述节点之一更新与所述节点之一相对应的向量的分量。 更新包括通过散列所接收的数据分组的属性来选择要更新的向量的分量。

    SCALABLE METHODS FOR DETECTING SIGNIFICANT TRAFFIC PATTERNS IN A DATA NETWORK
    49.
    发明申请
    SCALABLE METHODS FOR DETECTING SIGNIFICANT TRAFFIC PATTERNS IN A DATA NETWORK 有权
    用于检测数据网络中重要交通模式的可扩展方法

    公开(公告)号:US20090006607A1

    公开(公告)日:2009-01-01

    申请号:US11770430

    申请日:2007-06-28

    Abstract: Methods and apparatuses are provided for detecting traffic patterns in a data network. A sequential hashing scheme can be utilized that has D hash arrays. Each hash array i, wherein 1≦i≦D, includes Mi independent hash tables each having K buckets, with each of the buckets having an associated traffic total. Each of the keys corresponds with a single bucket of each of the Mi independent hash tables of each hash array i. The keys of the data network are partitioned into D words. As traffic is received for a key, a traffic total of each bucket that corresponds with a key is updated. The hash arrays can then be utilized to identify high traffic buckets of the independent hash tables having a traffic total greater than a threshold value. The high traffic buckets can be used to detect significant traffic patterns of the data network.

    Abstract translation: 提供了用于检测数据网络中的流量模式的方法和装置。 可以使用具有D个散列数组的顺序散列方案。 每个散列数组i,其中1 <= i <= D,包括各自具有K个桶的独立于Mi的哈希表,其中每个桶具有相关联的业务量。 每个密钥对应于每个散列数组i的每个Mi独立哈希表的单个桶。 数据网络的密钥分为D个字。 当一个密钥接收到流量时,更新与密钥对应的每个桶的流量总和。 然后可以使用散列数组来识别具有大于阈值的流量总和的独立散列表的高流量桶。 高流量桶可用于检测数据网络的重要流量模式。

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